Color deviations in production arise from variation in material, process, measurement, or specification. A structural analysis is necessary to move from symptom treatment to cause-oriented solutions. This article provides a technical framework for diagnosing color deviations using standardized methods and instrumental data.
Systematic Approach: Five Core Steps
An effective analysis of color deviations consists of the following steps:
- Visual description of the deviation in CIELAB terms.
- Objective measurement with spectrophotometry.
- Process control: evaluation of parameters such as temperature, pressure, dwell time, and application method.
- Raw material analysis: control of pigments, batch differences, and supplier documentation.
- Documentation of all data for traceability and comparability.
Why Root Cause Investigation Is Crucial
Studies show that a large portion of color problems are recurring because corrections target symptoms rather than causes. Variations in process conditions, material batches, or assessment conditions lead to repetition of deviations when structural measures are absent.
Step 1: Specification of the Visual Deviation
Use standardized CIELAB description:
- L*: too light / too dark
- a*: too red / too green
- b*: too yellow / too blue
Also analyze the pattern:
- Consistent within batch → possible raw material or formula variation.
- Inconsistent within batch → indication of process parameters or application variation.
Uniform terminology improves internal communication and traceability.
Step 2: Context Analysis
Relevant technical parameters:
Process Conditions
- temperature, pressure, dwell time
- spray or application method
- pH and dye bath conditions (textiles)
Approximately 42% of color deviations are directly linked to process variation.
Raw Materials
- batch differences in pigments or additives
- variation within permitted specification
- stability of pigments under production conditions
Environmental Factors
- humidity
- temperature
- atmospheric degradation of pigments
Traceability of parameters is essential for correlation analysis.
Step 3: Categorization of Possible Causes
Color deviations typically fall within four categories:
1. Material-Related
- variation in pigment concentration or dispersion
- differences between base materials
- incompatibility of additives
2. Process-Related
- variation in temperature, pressure, dwell time
- unstable application or drying process
3. Measurement-Related
- calibration deviations
- differences in measurement geometry (SCI/SCE, d/8, multi-angle)
- inconsistencies in lighting conditions
4. Specification-Related
- unclear tolerance limits
- unrealistic ΔE specifications
Step 4: Root Cause Analysis Methods
Two techniques provide structural depth:
5-Why Analysis
An iterative process of questioning until the underlying cause is established (for example, the absence of a work protocol, deviating dosing logic, or non-standardized assessment).
Example:
- Why is the color too red? → Pigment dosage too high
- Why was dosage too high? → Deviating calibration
- Why was calibration deviating? → Maintenance skipped
- Why was maintenance skipped? → No planning
- Why no planning? → Absence of preventive maintenance protocol
Ishikawa / Fishbone Diagram
Structuring of causes into categories:
Man – Machine – Material – Method – Measurement – Environment.
This method prevents tunnel vision and makes multifactorial causes visible.
Step 5: Structural Correction and Prevention
Preventive Measures
- improvement of incoming raw material control
- process optimization via Design of Experiments
- standardization of measurement protocols and assessment conditions
Corrective Measures
- formula adjustments based on spectral data
- process parameter optimization
- implementation of feed-forward controls based on raw material measurements
Validation
- controlled verification tests
- assurance via documentation and transfer within the organization
Practical Example: Textile Dyeing
Problem: Inconsistent red tint in polyester batch
Analysis:
- ΔL*: +2.1 (too light)
- Δa*: -1.8 (too green)
- Δb*: +0.4
5-Why result: pH value of dye bath not controlled → no daily calibration of pH meter
Solution: Implementation of daily pH control + automatic buffer dosing
Result: ΔE variation decreased from 3.2 to 0.8
Conclusion
A systematic approach to color deviations makes it possible to accurately identify causes and minimize recurrence. By combining visual analysis with instrumental measurement, process control, and standardized methods, a robust diagnostic system emerges. This contributes to higher predictability, lower failure costs, and better color consistency within production environments.
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References
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